Chronic heart disease condition development trend prediction system

Abstract

The invention relates to the technical field of medical health information, in particular to a chronic heart disease condition development trend prediction system, which comprises a data acquisition module for acquiring real-time data and historical data of a patient; the data processing module is used for preprocessing the collected real-time data and historical data; the weight calculation module is used for calculating the weight of each data point in the historical data collected by the data collection module to the prediction result according to the historical data based on a machine learning algorithm; and the prediction module is used for predicting the chronic heart disease development trend of the patient according to the weight obtained by the weight calculation module, in combination with a prediction algorithm and by utilizing the real-time data acquired in the data acquisition module. According to the method, individual differences, real-time data changes and historical data of the patients are comprehensively considered, so that the conditions of the patients are personalized and accurately predicted, and the prediction accuracy and reliability are improved.

Description

Technical Field

[0001] The present invention relates to the field of medical health information technology, and in particular to a chronic heart disease disease development trend prediction system. Background Art

[0002] Chronic heart disease, as a common and serious chronic disease, has long been an important concern in the medical field. The complexity and uncertainty of its development make the prediction of its development trend a very challenging task. Traditional prediction methods mainly rely on the experience of doctors and the clinical manifestations of patients, but these methods often have the problems of strong subjectivity and insufficient accuracy.

[0003] With the rapid development of medical information technology, especially the widespread application of big data, machine learning and artificial intelligence technology, new opportunities have been provided for the prediction of chronic heart disease. By analyzing multi-dimensional information such as patients' real-time physiological data, historical medical records and genetic factors, the system can dig out potential patterns and risk factors related to the development of the disease.

[0004] However, most existing prediction systems use a fixed weight allocation method, ignoring the different effects of different data points on prediction results due to individual differences among patients. This method often fails to accurately reflect the personalized characteristics of patients, resulting in limited accuracy and reliability of prediction results. In addition, some systems lack the ability to collect and process real-time data, and are unable to update prediction results in a timely manner, making it difficult to meet clinical needs.

[0005] Therefore, there is an urgent need for a chronic heart disease development trend prediction system that can comprehensively consider individual differences among patients, real-time data changes, and historical data patterns to improve the accuracy and reliability of the prediction. Summary of the invention

[0006] To solve the above problems, the present invention provides a chronic heart disease development trend prediction system, which comprehensively considers individual differences among patients, real-time data changes and historical data to achieve personalized and accurate prediction of the patient's condition, thereby improving the accuracy and reliability of the prediction.

[0007] In order to achieve the above-mentioned object, the technical scheme of the present invention is as follows: A system for predicting the development trend of chronic heart disease, comprising: Data collection module, used to collect patients' real-time and historical data; Data processing module, used for preprocessing the collected real-time data and historical data; A weight calculation module is used to calculate the weight of each data point in the historical data collected in the data collection module for the prediction result based on the historical data based on the machine learning algorithm; The prediction module is used to predict the development trend of the patient's chronic heart disease based on the weights obtained by the weight calculation module, combined with the prediction algorithm, and using the real-time data collected in the data acquisition module.

[0008] The technical principle of the above solution is as follows: Machine learning technology is used to establish a prediction model through analysis and learning of historical data. The weight calculation module in the model is the key, which can dynamically adjust the weight of each data point to more accurately reflect the impact of each feature variable on the prediction result. The prediction module uses these weights and real-time data to output the prediction result through the prediction algorithm.

[0009] The above scheme has the following beneficial effects: 1. This solution achieves personalized prediction of the patient's condition by dynamically adjusting the weight of each data point. Compared with the traditional fixed weight method, this personalized prediction can more accurately reflect the patient's actual situation and improve the accuracy and reliability of the prediction.

[0010] 2. In this solution, through the analysis and learning of historical data by machine learning algorithms, the system can automatically discover the patterns and trends in the data, thereby improving the accuracy of predictions. This automated and intelligent prediction method is more accurate and efficient than traditional manual predictions.

[0011] 3. This solution supports the collection and processing of real-time data, and can update the prediction results in time as the patient's condition changes. This helps doctors understand the patient's health status in a timely manner and develop more effective treatment plans.

[0012] 4. This solution, by providing accurate prediction results, the system can provide strong decision support for doctors. Doctors can formulate personalized treatment plans based on the prediction results to improve the efficiency and quality of medical decision-making.

[0013] Furthermore, the real-time data and historical data collected by the data collection module include medical records, medication status and lifestyle.

[0014] Beneficial effects: By collecting multi-dimensional data such as medical records, medication use, and lifestyle, the system can more comprehensively assess the patient's chronic heart disease condition. Together, these data form a complete picture of the patient's condition development, helping doctors to more accurately judge the patient's health status. Based on the patient's personal medical records, medication use, and lifestyle data, the system can generate personalized treatment plan recommendations. These recommendations not only take into account the patient's current condition, but also combine the patient's personal habits and preferences, thereby improving the feasibility and effectiveness of the treatment plan.

[0015] Furthermore, when the prediction module predicts the development trend, the medical data in the real-time data is used as the basic data. Based on the basic data, combined with the weights of each data point obtained by the weight calculation module and the prediction algorithm, a comprehensive analysis is performed to predict the future development trend of the patient's chronic heart disease.

[0016] Beneficial effects: The prediction module is based on real-time medical data, which directly reflects the patient's current physiological state and condition. Combined with the weights of each data point obtained by the weight calculation module, the prediction module can more accurately evaluate the impact of different data points on the prediction results, thereby improving the accuracy of the prediction. Each patient's chronic heart disease condition is unique and their development trends are also different. By analyzing the patient's real-time medical data, combined with their historical data and weight information, the prediction module can generate personalized prediction results and provide patients with more accurate health management suggestions.

[0017] Furthermore, preprocessing in the data processing module includes cleaning, sorting and formatting of real-time data and historical data, as well as missing value filling, outlier detection and processing, and data normalization.

[0018] Beneficial effects: Cleaning, collating and formatting steps can remove redundant, repeated and irrelevant information in the data, ensuring the accuracy and consistency of the data. Missing value filling and outlier detection and processing can solve the problems of incomplete and abnormal data and reduce the impact of data errors on prediction results. Data normalization converts data of different magnitudes to the same magnitude, which helps to eliminate the dimensional differences between data and improve the stability and generalization ability of the model. The preprocessed data is more standardized, which reduces the noise interference in the model training process and enhances the robustness of the model. The preprocessed data is purer, complete and consistent, providing high-quality data input for the weight calculation module and prediction module.

[0019] Furthermore, it also includes a risk identification module, which automatically analyzes the patient's real-time data based on the results of the weight calculation model and the preset model, as well as the preset health goals, and identifies factors that may increase the risk of a heart attack.

[0020] Beneficial effects: The risk identification module can monitor patient data in real time, and once an abnormality or potential risk is found, it will immediately issue an early warning, providing doctors with timely intervention opportunities. This helps doctors take necessary preventive measures before a heart attack and reduce patients' health risks. By analyzing patients' real-time data, the risk identification module can identify individual differences and specific risk factors of patients. This provides doctors with a basis for developing personalized health management plans, which helps patients better manage their health conditions. The risk identification module combines the results of the weight calculation model and the preset model to more comprehensively assess patients' risk of heart attacks. The risk identification module can provide patients with real-time feedback and early warning information about their health conditions. This helps to enhance patients' awareness of self-management, encourage patients to participate more actively in the health management process, and improve patients' health literacy and self-management capabilities.

[0021] Furthermore, it also includes a user interaction module, which is used to display prediction results, historical data, real-time data and the operating status of the system. The user interaction module is also used for patients and medical staff to input real-time information.

[0022] Beneficial effects: The user interaction module can display the prediction results, historical data and real-time data in a graphical and intuitive way, so that users can understand the patient's health status and disease development trends at a glance. This intuitive way of displaying information helps users understand the data more quickly and make more informed decisions. Doctors can view the patient's detailed data and prediction results through the user interaction module, so as to have a more comprehensive understanding of the patient's condition. This helps doctors communicate effectively with patients during the diagnosis and treatment process, explain the condition, develop treatment plans, and answer patients' questions. The user interaction module can display the system's operating status in real time, including the progress and results of each link such as data collection, processing, and prediction. This helps users understand the system's operating status, discover and solve problems in a timely manner, and improve the stability and reliability of the system.

[0023] Furthermore, it also includes an intelligent decision-making module, which is used to identify risk factors based on the risk identification module, automatically generate personalized health reminders and health plans, and send them to patients through the user interaction module.

[0024] Beneficial effects: The intelligent decision-making module can tailor health reminders and health plans for patients according to their specific conditions and risk factors. This personalized health management method can more accurately meet the health needs of patients and improve the pertinence and effectiveness of health management. Once the risk identification module identifies potential risk factors for heart disease, the intelligent decision-making module can quickly generate corresponding health reminders. These reminders can help patients realize potential health risks in a timely manner and take corresponding preventive measures to avoid or delay the onset of heart disease. By receiving health reminders and health plans sent by the intelligent decision-making module, patients can participate more actively in the health management process. This sense of participation can enhance patients' self-management awareness and improve their enthusiasm and consciousness in complying with health recommendations. The intelligent decision-making module can continuously update health reminders and health plans based on patients' real-time data and changes in their condition. This dynamic adjustment method ensures that patients always receive the health advice that best suits their current health status, thereby improving the effectiveness of health management.

[0025] Furthermore, the user interaction module is also used for patient education, providing patients with educational content about their condition, treatment options and healthy lifestyles through a variety of media forms such as charts, animations and videos.

[0026] Beneficial effects: The user interaction module helps patients better understand their condition, treatment options, and the importance of a healthy lifestyle through intuitive and vivid multimedia content. This educational approach can significantly improve patients' knowledge and understanding of heart disease and enhance their awareness and ability to manage their own health. The provision of patient education content provides more common topics and background knowledge for communication between patients and doctors. This helps doctors explain the condition and develop treatment plans more smoothly, and also makes it easier for patients to understand and accept the doctor's advice. By learning the educational content in the user interaction module, patients can master more self-management skills and methods. These skills and methods help patients better monitor their health status and take timely measures to deal with potential health risks. The educational content in the user interaction module not only focuses on the condition and treatment options, but also emphasizes the importance of a healthy lifestyle. By learning and practicing these educational contents, patients can gradually develop healthy living habits, thereby reducing the risk of heart attacks.

[0027] Furthermore, it also includes a feedback module, which is used to receive real-time information input by the user interaction module, and use the real-time information as verification information of the prediction results, and adjust and train the weight calculation module and the prediction module according to the verification information.

[0028] Beneficial effects: The feedback module can promptly detect errors and deviations in the prediction results by receiving verification information. By adjusting and training the weight calculation module and the prediction module, the system can continuously learn and optimize, thereby improving the accuracy of the prediction. With the passage of time and the progress of medical research, the pathogenesis and risk factors of heart disease may change. The feedback module can continuously receive new verification information and adjust the model based on this information so that the model can adapt to these changes and maintain the effectiveness of its predictions. The weight calculation module plays a key role in prediction, which determines the contribution of different risk factors to the prediction results. The feedback module can optimize the distribution of weights through feedback of verification information, so that the prediction results are more in line with the actual situation.

[0029] Furthermore, it also includes a data security module, which is used to encrypt, store and transmit the collected real-time data and historical data. The data security module is also used to monitor the access log of the system.

[0030] Beneficial effects: The data security module effectively prevents data leakage and abuse by encrypting the storage and transmission of patient data. This helps protect patients' personal privacy and enhances their trust in the system. Encryption technology not only protects the confidentiality of data, but also ensures the integrity of data. During the data transmission and storage process, any tampering with the data will be immediately discovered, thus ensuring the authenticity and reliability of the data. The data security module monitors the system's access logs and can promptly detect and block unauthorized access and operations. This helps improve the overall security of the system and prevent malicious attacks and illegal intrusions.

[0031] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a framework diagram of an embodiment of a system for predicting the development trend of a chronic heart disease according to the present invention; Figure 2 This is a flow chart of an embodiment of a system for predicting the development trend of a chronic heart disease according to the present invention. DETAILED DESCRIPTION

[0033] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0034] The following is further described in detail through specific implementation methods: Embodiment 1:

[0035] As attached Figure 1 and Figure 2 Shown: A chronic heart disease development trend prediction system, including a data acquisition module, a data processing module, a weight calculation module and a prediction module.

[0036] In this embodiment, the data acquisition module can use wearable devices such as bracelets or other medical monitoring devices, which are mainly used to collect real-time data and historical data of patients; the real-time data and historical data collected include medical records (such as electrocardiogram results, blood pressure readings and blood test reports, etc.), medication conditions (such as drug name, dosage, medication frequency and compliance) and lifestyle (such as eating habits, exercise frequency, smoking and drinking habits and sleep quality, etc.). At the same time, the data acquisition module is configured with data interfaces with other medical information systems (such as hospital electronic medical record systems) to obtain the patient's historical data. In this embodiment, the historical data includes past medical history, previous laboratory test results, historical medication data and historical lifestyle data.

[0037] The data processing module is used to preprocess the collected real-time data and historical data; preprocessing includes cleaning (removing duplicate and invalid data), sorting and formatting real-time data and historical data, as well as filling missing values ​​(using methods such as mean, median or interpolation), outlier detection (identification and processing through statistical methods or machine learning models) and processing and data normalization (scaling the data to the same range for unified processing and analysis).

[0038] The weight calculation module is used to calculate the weight of each data point in the historical data collected in the data collection module for the prediction result based on the historical data based on the machine learning algorithm (such as random forest, gradient boosting tree or neural network, etc.). The prediction model is trained using the calculated weight and the preprocessed historical data.

[0039] The prediction module is used to predict the development trend of the patient's chronic heart disease based on the weights obtained by the weight calculation module, combined with the prediction algorithm (such as time series analysis, support vector machine or deep learning model, etc.), and the real-time data collected in the data acquisition module. When predicting the development trend, the medical data in the real-time data is used as the basic data. Based on the basic data, combined with the weights of each data point obtained by the weight calculation module and the prediction algorithm, the future development trend of the patient's chronic heart disease is comprehensively analyzed and predicted.

[0040] The specific implementation process is as follows: The hospital has equipped patients A and B with a smart bracelet as a data collection device. The smart bracelet integrates functions such as heart rate monitoring, blood pressure measurement, blood oxygen saturation detection, and step counting, and can collect patients' physiological data in real time. Through the smart bracelet, medical record data such as electrocardiogram results (including heart rate variability, ST segment changes, etc.), blood pressure readings (systolic and diastolic blood pressure), and blood test reports (such as blood sugar, blood lipids, hemoglobin, myocardial enzyme spectrum, and myocardial markers, etc.) of patients A and B are collected in real time. The built-in sensors of the bracelet or synchronization with the patient's mobile phone application record the drug name, dosage, medication time and frequency, and medication compliance of patients A and B. At the same time, through the activity tracking function of the bracelet and the patient's self-report, the data collection module collects the eating habits of patients A and B (such as daily calorie intake, nutritional balance), exercise frequency (such as the number of exercise days per week, exercise type and intensity), smoking and drinking habits (such as daily smoking, drinking frequency and type), and sleep quality (such as sleep time, sleep duration, and deep sleep ratio). The data acquisition module is also connected to the hospital's electronic medical record system through a data interface, automatically obtaining detailed medical records of patients A and B over the past few years, including previous heart examination reports (such as echocardiograms, heart CT, etc.), medication records (including the use of prescription drugs, over-the-counter drugs and health products), hospitalization records and discharge summaries, etc. At the same time, historical data covers the patient's detailed medical records in the past, such as previous heart examination reports (such as echocardiograms, heart CT, etc.), medication records (including the use of prescription drugs, over-the-counter drugs and health products), hospitalization records and discharge summaries, etc. In addition, it also includes past physiological data collected through devices such as smart bracelets, such as heart rate, blood pressure, blood oxygen saturation, etc., as well as lifestyle data, such as eating habits, exercise frequency, smoking and drinking habits, and sleep quality.

[0041] In the data processing module, the data collected in the data acquisition module are firstly removed from duplicate, invalid or inconsistent data records to ensure the accuracy and consistency of the data. For missing data points, the data processing module reasonably fills them according to the mean, median or interpolation method of the previous and next data points to reduce the impact of missing data on the prediction results. The data processing module identifies and processes outliers through statistical methods (such as box plots) or machine learning models (such as isolation forests) to ensure the rationality of the data. All feature data are scaled to the same range (such as 0-1 or -1 to 1) for subsequent unified processing and analysis.

[0042] In the weight calculation module, the random forest algorithm was used to calculate the weight of each feature (such as heart rate, blood pressure, blood sugar level, etc.) for predicting the development trend of chronic heart disease based on the historical data of patients A and B.

[0043] For example, through in-depth analysis of patient A's historical data, it was found that changes in his blood pressure and blood sugar levels were closely related to the development of chronic heart disease. Specifically, whenever patient A's blood pressure exceeded a certain critical value or his blood sugar level fluctuated greatly, his heart disease symptoms tended to intensify or worsen. Therefore, the system sets the weights of these features (blood pressure and blood sugar levels) relatively high to ensure that these key signals can be accurately captured during the prediction process. In contrast, although dietary habits are equally important for patients with chronic heart disease, the system found that although the intake of certain specific types of food (such as high-salt and high-sugar foods) in patient A's dietary habits has changed, these changes are not as directly related to the development of the disease as blood pressure and blood sugar levels. Therefore, the system sets the weights of these secondary features (such as certain specific types of food in dietary habits) relatively low to avoid them from causing too much interference in the prediction process.

[0044] For patient B, by analyzing his historical data, the system found that medication records (especially the use of specific drugs and dosage adjustments) have an important impact on the development of patient B's chronic heart disease. Whenever patient B adjusts the dosage of drugs or changes drugs, his heart disease symptoms often improve or worsen significantly. Therefore, the system sets the weights of these features (medication records) relatively high. However, although exercise is an important rehabilitation method for patients with chronic heart disease, the system found that the change in patient B's exercise habits is not directly related to the development of his condition. It may be because patient B's exercise volume is relatively small, or the exercise method does not have a significant impact on his heart health. Therefore, the system sets the weights of these secondary features (such as exercise) relatively low. Then, using the calculated weights and preprocessed historical data, the system trained a deep learning prediction model. The model selected the LSTM (Long Short-Term Memory Network) algorithm suitable for processing time series data, and adjusted the model parameters (such as learning rate, batch size, number of iterations, etc.) according to the algorithm requirements.

[0045] After the model is trained, the system inputs the real-time data of patients A and B into the prediction module. The prediction module predicts the development trend of chronic heart disease in patients A and B based on the weights obtained by the weight calculation module and the trained deep learning model. The prediction results show that the heart disease of patients A and B is at risk of worsening in the next few months, especially blood pressure and blood sugar levels need to be closely monitored. The system also generates a detailed report based on the prediction results, including a chart of the development trend of the disease, risk level prompts, and recommended treatment plans and lifestyle adjustment suggestions.

[0046] Embodiment 2:

[0047] The difference from Example 1 is that it also includes a risk identification module, which automatically analyzes the patient's real-time data based on the results of the weight calculation model and the preset model, as well as the preset health goals, and identifies factors that may increase the risk of heart attack (such as increased blood pressure, abnormal heart rate, etc.).

[0048] It also includes a user interaction module, which is used to display prediction results, historical data, real-time data, and the operating status of the system. The user interaction module is also used for patient education, providing patients with educational content about their condition, treatment plan, and healthy lifestyle through a variety of media forms such as charts, animations, and videos.

[0049] It also includes an intelligent decision-making module, which is used to identify risk factors based on the risk identification module, automatically generate personalized health reminders and health plans, and send them to patients through the user interaction module.

[0050] The specific implementation process is as follows: Based on the data collection module and the weight calculation module, the risk identification module automatically receives real-time data and weight calculation results, and at the same time, the module loads preset health goals (key health indicators such as heart rate range, blood pressure range, blood sugar level and exercise frequency). The risk identification module uses machine learning algorithms (such as logistic regression, decision trees, etc.), combined with the results of the weight calculation model and the preset model, to conduct in-depth analysis of real-time data, and can identify factors that may increase the risk of heart attack, such as abnormally high blood pressure, blood sugar fluctuations, irregular medication, etc. Based on the identified risk factors, the risk identification module further evaluates the patient's risk level (low, medium, high or urgent, etc.) so that doctors and patients can quickly understand the current health status and take corresponding measures.

[0051] The user interaction module displays the prediction results of the chronic heart disease development trend output by the prediction module (including the development trend of the disease in the future, possible risk points and corresponding risk levels) through intuitive charts and reports. It also displays the patient's historical data and real-time data, including medical records such as electrocardiogram, blood pressure, blood sugar, as well as data such as medication status and lifestyle. The display methods may include line graphs, bar graphs, pie charts, etc., so that patients and doctors can intuitively understand the changing trends of the data.

[0052] The user interaction module provides patients with educational content about their condition, treatment options and healthy lifestyles through various media forms such as charts, animations and videos. The educational content may include the causes, symptoms, prevention measures, treatment methods, etc. of heart disease, as well as how to improve health by adjusting diet, increasing exercise, etc.

[0053] The intelligent decision-making module automatically generates personalized health reminders (including medication reminders, exercise reminders, dietary adjustment suggestions, etc.) based on the risk factors and risk levels identified by the risk identification module, aiming to help patients correct bad living habits in a timely manner and reduce the risk of heart attacks. It also develops personalized health plans for patients based on risk factors and risk levels (including detailed diet plans, exercise plans, medication plans, etc.) to comprehensively improve the health of patients. Health reminders and health plans are sent to patients through the user interaction module. The sending methods may include SMS, email, APP push, etc., so that patients can receive and view relevant information at any time.

[0054] Embodiment 3:

[0055] The difference from Example 2 is that it also includes a feedback module. The user interaction module is also used for patients and medical staff to input real-time information. The feedback module is used to receive the real-time information input by the user interaction module, and use the real-time information as verification information for the prediction results. The weight calculation module and the prediction module are adjusted and trained according to the verification information.

[0056] The specific implementation process is as follows: The feedback module receives the prediction result verification information (real-time information) from doctors, patients or other verification sources in the user interaction module. This verification information may include the patient's actual disease progression, treatment effects, and medication feedback. Compare the prediction results with the verification information to evaluate the accuracy of the prediction model. According to the feedback of the verification information, adjust and train the weight calculation module and the prediction module. This may include adjusting the weights of feature variables, optimizing machine learning algorithms, adding new data samples, etc. Regularly monitor the system's operating status and prediction accuracy, and update and optimize as needed to improve prediction accuracy.

[0057] Embodiment 4:

[0058] The difference from Example 3 is that it also includes a data security module, which is used to encrypt, store and transmit the collected real-time data and historical data, and is also used to monitor the access log of the system.

[0059] The specific implementation process is as follows: Data security module: encrypt and store the collected real-time data and historical data to ensure the security of the data during storage. This can be achieved by using advanced encryption algorithms and encryption technologies, such as AES, RSA, etc.

[0060] During data transmission, encryption technology is used to ensure data security. This can be achieved by using secure transmission protocols such as SSL / TLS protocols to prevent data from being stolen or tampered with during transmission.

[0061] Monitor the system's access logs and record all access behaviors to the system, including access time, access user, access type (such as viewing, modifying, deleting, etc.) and access results. This helps to promptly detect and prevent illegal access and data leakage. Audit the access logs regularly to check for abnormal access behaviors. At the same time, set up a security alarm mechanism to issue an alarm message in a timely manner when abnormal access behaviors are detected so that corresponding security measures can be taken.

[0062] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

Claims

1. A chronic heart disease disease development trend prediction system, characterized in that: include: Data collection module, used to collect patients' real-time and historical data; Data processing module, used for preprocessing the collected real-time data and historical data; A weight calculation module is used to calculate the weight of each data point in the historical data collected in the data collection module for the prediction result based on the historical data based on the machine learning algorithm; The prediction module is used to predict the development trend of the patient's chronic heart disease based on the weights obtained by the weight calculation module, combined with the prediction algorithm, and using the real-time data collected in the data acquisition module.

2. The chronic heart disease development trend prediction system according to claim 1, characterized in that: The real-time and historical data collected by the data collection module include medical records, medication usage and lifestyle.

3. The chronic heart disease condition development trend prediction system according to claim 2, characterized in that: When the prediction module predicts the development trend, it uses the medical data in the real-time data as the basic data. Based on the basic data, combined with the weights of each data point obtained by the weight calculation module and the prediction algorithm, it comprehensively analyzes and predicts the future development trend of the patient's chronic heart disease.

4. The chronic heart disease condition development trend prediction system according to claim 3, characterized in that: The preprocessing in the data processing module includes cleaning, sorting and formatting of real-time data and historical data, as well as missing value filling, outlier detection and processing, and data normalization.

5. The chronic heart disease development trend prediction system according to claim 4, characterized in that: It also includes a risk identification module, which automatically analyzes the patient's real-time data based on the results of the weight calculation model and the preset model, as well as the preset health goals, and identifies factors that may increase the risk of a heart attack.

6. The chronic heart disease development trend prediction system according to claim 5, characterized in that: It also includes a user interaction module, which is used to display prediction results, historical data, real-time data and the operating status of the system. The user interaction module is also used for patients and medical staff to input real-time information.

7. The chronic heart disease development trend prediction system according to claim 6, characterized in that: It also includes an intelligent decision-making module, which is used to identify risk factors based on the risk identification module, automatically generate personalized health reminders and health plans, and send them to patients through the user interaction module.

8. The chronic heart disease development trend prediction system according to claim 7, characterized in that: The user interaction module is also used for patient education, providing patients with educational content about their condition, treatment options and healthy lifestyles through a variety of media forms such as charts, animations and videos.

9. The chronic heart disease development trend prediction system according to claim 8, characterized in that: It also includes a feedback module, which is used to receive real-time information input by the user interaction module, and use the real-time information as verification information for the prediction results, and adjust and train the weight calculation module and the prediction module according to the verification information.

10. The chronic heart disease development trend prediction system according to claim 9, characterized in that: It also includes a data security module, which is used to encrypt, store and transmit the collected real-time data and historical data. The data security module is also used to monitor the access log of the system.

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